Overview

Dataset statistics

Number of variables18
Number of observations35064
Missing cells5279
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 MiB
Average record size in memory144.0 B

Variable types

Numeric15
Categorical3

Alerts

station has constant value ""Constant
CO is highly overall correlated with NO2 and 3 other fieldsHigh correlation
DEWP is highly overall correlated with PRES and 1 other fieldsHigh correlation
NO2 is highly overall correlated with CO and 5 other fieldsHigh correlation
No is highly overall correlated with yearHigh correlation
O3 is highly overall correlated with NO2 and 1 other fieldsHigh correlation
PM10 is highly overall correlated with CO and 3 other fieldsHigh correlation
PM2.5 is highly overall correlated with CO and 3 other fieldsHigh correlation
PRES is highly overall correlated with DEWP and 1 other fieldsHigh correlation
SO2 is highly overall correlated with CO and 3 other fieldsHigh correlation
TEMP is highly overall correlated with DEWP and 2 other fieldsHigh correlation
WSPM is highly overall correlated with NO2High correlation
year is highly overall correlated with NoHigh correlation
PM2.5 has 616 (1.8%) missing valuesMissing
PM10 has 429 (1.2%) missing valuesMissing
SO2 has 474 (1.4%) missing valuesMissing
NO2 has 659 (1.9%) missing valuesMissing
CO has 1753 (5.0%) missing valuesMissing
O3 has 1173 (3.3%) missing valuesMissing
RAIN is highly skewed (γ1 = 34.71933563)Skewed
No is uniformly distributedUniform
No has unique valuesUnique
hour has 1461 (4.2%) zerosZeros
RAIN has 33664 (96.0%) zerosZeros
WSPM has 1353 (3.9%) zerosZeros

Reproduction

Analysis started2024-03-08 05:10:24.121740
Analysis finished2024-03-08 05:11:03.813935
Duration39.69 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

No
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct35064
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17532.5
Minimum1
Maximum35064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:11:03.988568image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1754.15
Q18766.75
median17532.5
Q326298.25
95-th percentile33310.85
Maximum35064
Range35063
Interquartile range (IQR)17531.5

Descriptive statistics

Standard deviation10122.249
Coefficient of variation (CV)0.57734204
Kurtosis-1.2
Mean17532.5
Median Absolute Deviation (MAD)8766
Skewness0
Sum6.1475958 × 108
Variance1.0245993 × 108
MonotonicityStrictly increasing
2024-03-08T12:11:04.288793image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
23379 1
 
< 0.1%
23373 1
 
< 0.1%
23374 1
 
< 0.1%
23375 1
 
< 0.1%
23376 1
 
< 0.1%
23377 1
 
< 0.1%
23378 1
 
< 0.1%
23380 1
 
< 0.1%
23422 1
 
< 0.1%
Other values (35054) 35054
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
35064 1
< 0.1%
35063 1
< 0.1%
35062 1
< 0.1%
35061 1
< 0.1%
35060 1
< 0.1%
35059 1
< 0.1%
35058 1
< 0.1%
35057 1
< 0.1%
35056 1
< 0.1%
35055 1
< 0.1%

year
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size274.1 KiB
2016
8784 
2014
8760 
2015
8760 
2013
7344 
2017
1416 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters140256
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013
2nd row2013
3rd row2013
4th row2013
5th row2013

Common Values

ValueCountFrequency (%)
2016 8784
25.1%
2014 8760
25.0%
2015 8760
25.0%
2013 7344
20.9%
2017 1416
 
4.0%

Length

2024-03-08T12:11:04.566134image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T12:11:05.155899image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 8784
25.1%
2014 8760
25.0%
2015 8760
25.0%
2013 7344
20.9%
2017 1416
 
4.0%

Most occurring characters

ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 140256
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 140256
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5229295
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:11:05.365210image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4487524
Coefficient of variation (CV)0.52871219
Kurtosis-1.2080577
Mean6.5229295
Median Absolute Deviation (MAD)3
Skewness-0.0092942217
Sum228720
Variance11.893893
MonotonicityNot monotonic
2024-03-08T12:11:05.525516image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 2976
8.5%
5 2976
8.5%
7 2976
8.5%
8 2976
8.5%
10 2976
8.5%
12 2976
8.5%
1 2976
8.5%
4 2880
8.2%
6 2880
8.2%
9 2880
8.2%
Other values (2) 5592
15.9%
ValueCountFrequency (%)
1 2976
8.5%
2 2712
7.7%
3 2976
8.5%
4 2880
8.2%
5 2976
8.5%
6 2880
8.2%
7 2976
8.5%
8 2976
8.5%
9 2880
8.2%
10 2976
8.5%
ValueCountFrequency (%)
12 2976
8.5%
11 2880
8.2%
10 2976
8.5%
9 2880
8.2%
8 2976
8.5%
7 2976
8.5%
6 2880
8.2%
5 2976
8.5%
4 2880
8.2%
3 2976
8.5%

day
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.729637
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:11:05.742194image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8002175
Coefficient of variation (CV)0.55946729
Kurtosis-1.1940295
Mean15.729637
Median Absolute Deviation (MAD)8
Skewness0.0067598056
Sum551544
Variance77.443829
MonotonicityNot monotonic
2024-03-08T12:11:06.036999image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 1152
 
3.3%
2 1152
 
3.3%
28 1152
 
3.3%
27 1152
 
3.3%
26 1152
 
3.3%
25 1152
 
3.3%
24 1152
 
3.3%
23 1152
 
3.3%
22 1152
 
3.3%
21 1152
 
3.3%
Other values (21) 23544
67.1%
ValueCountFrequency (%)
1 1152
3.3%
2 1152
3.3%
3 1152
3.3%
4 1152
3.3%
5 1152
3.3%
6 1152
3.3%
7 1152
3.3%
8 1152
3.3%
9 1152
3.3%
10 1152
3.3%
ValueCountFrequency (%)
31 672
1.9%
30 1056
3.0%
29 1080
3.1%
28 1152
3.3%
27 1152
3.3%
26 1152
3.3%
25 1152
3.3%
24 1152
3.3%
23 1152
3.3%
22 1152
3.3%

hour
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros1461
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:11:06.245135image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.9222853
Coefficient of variation (CV)0.60193785
Kurtosis-1.2041745
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum403236
Variance47.918033
MonotonicityNot monotonic
2024-03-08T12:11:06.521077image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 1461
 
4.2%
1 1461
 
4.2%
22 1461
 
4.2%
21 1461
 
4.2%
20 1461
 
4.2%
19 1461
 
4.2%
18 1461
 
4.2%
17 1461
 
4.2%
16 1461
 
4.2%
15 1461
 
4.2%
Other values (14) 20454
58.3%
ValueCountFrequency (%)
0 1461
4.2%
1 1461
4.2%
2 1461
4.2%
3 1461
4.2%
4 1461
4.2%
5 1461
4.2%
6 1461
4.2%
7 1461
4.2%
8 1461
4.2%
9 1461
4.2%
ValueCountFrequency (%)
23 1461
4.2%
22 1461
4.2%
21 1461
4.2%
20 1461
4.2%
19 1461
4.2%
18 1461
4.2%
17 1461
4.2%
16 1461
4.2%
15 1461
4.2%
14 1461
4.2%

PM2.5
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct557
Distinct (%)1.6%
Missing616
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean82.933372
Minimum2
Maximum680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:11:06.805294image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q123
median59
Q3115
95-th percentile243
Maximum680
Range678
Interquartile range (IQR)92

Descriptive statistics

Standard deviation80.933497
Coefficient of variation (CV)0.97588577
Kurtosis5.6001221
Mean82.933372
Median Absolute Deviation (MAD)41
Skewness1.9734646
Sum2856888.8
Variance6550.231
MonotonicityNot monotonic
2024-03-08T12:11:07.179619image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 574
 
1.6%
12 566
 
1.6%
10 550
 
1.6%
14 538
 
1.5%
9 517
 
1.5%
13 496
 
1.4%
8 477
 
1.4%
15 476
 
1.4%
16 451
 
1.3%
3 408
 
1.2%
Other values (547) 29395
83.8%
(Missing) 616
 
1.8%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 408
1.2%
4 194
 
0.6%
5 240
0.7%
6 337
1.0%
7 378
1.1%
8 477
1.4%
9 517
1.5%
10 550
1.6%
11 574
1.6%
ValueCountFrequency (%)
680 1
< 0.1%
666 1
< 0.1%
664 1
< 0.1%
663 1
< 0.1%
645 1
< 0.1%
639 1
< 0.1%
637 1
< 0.1%
636 1
< 0.1%
635 1
< 0.1%
633 1
< 0.1%

PM10
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct630
Distinct (%)1.8%
Missing429
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean109.0233
Minimum2
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:11:07.387182image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile13
Q140
median89
Q3149
95-th percentile281
Maximum999
Range997
Interquartile range (IQR)109

Descriptive statistics

Standard deviation91.573709
Coefficient of variation (CV)0.8399462
Kurtosis6.0181605
Mean109.0233
Median Absolute Deviation (MAD)53
Skewness1.8458503
Sum3776022.1
Variance8385.7441
MonotonicityNot monotonic
2024-03-08T12:11:07.648310image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 312
 
0.9%
14 303
 
0.9%
25 300
 
0.9%
20 292
 
0.8%
24 291
 
0.8%
23 288
 
0.8%
16 287
 
0.8%
21 274
 
0.8%
15 273
 
0.8%
22 270
 
0.8%
Other values (620) 31745
90.5%
(Missing) 429
 
1.2%
ValueCountFrequency (%)
2 7
 
< 0.1%
3 21
 
0.1%
4 16
 
< 0.1%
5 175
0.5%
6 243
0.7%
7 117
0.3%
8 186
0.5%
9 199
0.6%
10 208
0.6%
11 233
0.7%
ValueCountFrequency (%)
999 1
< 0.1%
987 1
< 0.1%
961 1
< 0.1%
917 1
< 0.1%
906 1
< 0.1%
828 1
< 0.1%
820 1
< 0.1%
814 1
< 0.1%
813 1
< 0.1%
807 1
< 0.1%

SO2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct280
Distinct (%)0.8%
Missing474
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean17.590941
Minimum1
Maximum293
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:11:07.956038image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median8
Q322
95-th percentile67
Maximum293
Range292
Interquartile range (IQR)19

Descriptive statistics

Standard deviation23.600367
Coefficient of variation (CV)1.3416204
Kurtosis11.799211
Mean17.590941
Median Absolute Deviation (MAD)6
Skewness2.8663252
Sum608470.67
Variance556.97731
MonotonicityNot monotonic
2024-03-08T12:11:08.204657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 7349
21.0%
3 2326
 
6.6%
4 1912
 
5.5%
5 1683
 
4.8%
6 1498
 
4.3%
7 1347
 
3.8%
8 1237
 
3.5%
9 1074
 
3.1%
10 952
 
2.7%
11 861
 
2.5%
Other values (270) 14351
40.9%
ValueCountFrequency (%)
1 133
 
0.4%
1.1424 1
 
< 0.1%
1.7136 2
 
< 0.1%
1.9992 3
 
< 0.1%
2 7349
21.0%
2.2848 7
 
< 0.1%
3 2326
 
6.6%
3.1416 1
 
< 0.1%
3.4272 1
 
< 0.1%
3.5 1
 
< 0.1%
ValueCountFrequency (%)
293 1
< 0.1%
277 1
< 0.1%
274 1
< 0.1%
263 1
< 0.1%
247 1
< 0.1%
240 1
< 0.1%
239 1
< 0.1%
238 1
< 0.1%
229 1
< 0.1%
221 1
< 0.1%

NO2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct376
Distinct (%)1.1%
Missing659
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean57.901643
Minimum2
Maximum270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:11:08.501384image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile15
Q131
median51
Q378
95-th percentile124
Maximum270
Range268
Interquartile range (IQR)47

Descriptive statistics

Standard deviation35.150857
Coefficient of variation (CV)0.60707876
Kurtosis1.1222321
Mean57.901643
Median Absolute Deviation (MAD)23
Skewness1.0277689
Sum1992106
Variance1235.5828
MonotonicityNot monotonic
2024-03-08T12:11:08.781989image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 491
 
1.4%
30 486
 
1.4%
21 481
 
1.4%
29 473
 
1.3%
26 465
 
1.3%
35 464
 
1.3%
24 463
 
1.3%
25 458
 
1.3%
36 451
 
1.3%
31 450
 
1.3%
Other values (366) 29723
84.8%
(Missing) 659
 
1.9%
ValueCountFrequency (%)
2 70
 
0.2%
3 21
 
0.1%
4 29
 
0.1%
5 35
 
0.1%
6 41
 
0.1%
7 58
 
0.2%
8 88
0.3%
9 115
0.3%
10 173
0.5%
11 178
0.5%
ValueCountFrequency (%)
270 1
 
< 0.1%
239 1
 
< 0.1%
233 1
 
< 0.1%
231 1
 
< 0.1%
230 1
 
< 0.1%
226 3
< 0.1%
225 2
< 0.1%
224 1
 
< 0.1%
223 1
 
< 0.1%
222 1
 
< 0.1%

CO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct117
Distinct (%)0.4%
Missing1753
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean1271.2944
Minimum100
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:11:09.138785image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile300
Q1500
median900
Q31600
95-th percentile3600
Maximum10000
Range9900
Interquartile range (IQR)1100

Descriptive statistics

Standard deviation1164.8549
Coefficient of variation (CV)0.91627476
Kurtosis9.1945221
Mean1271.2944
Median Absolute Deviation (MAD)499
Skewness2.5382105
Sum42348087
Variance1356887
MonotonicityNot monotonic
2024-03-08T12:11:09.478757image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400 2417
 
6.9%
300 2341
 
6.7%
600 2330
 
6.6%
500 2299
 
6.6%
700 2218
 
6.3%
800 2043
 
5.8%
900 1926
 
5.5%
1000 1715
 
4.9%
1100 1504
 
4.3%
1200 1426
 
4.1%
Other values (107) 13092
37.3%
(Missing) 1753
 
5.0%
ValueCountFrequency (%)
100 481
 
1.4%
200 1159
3.3%
300 2341
6.7%
400 2417
6.9%
500 2299
6.6%
600 2330
6.6%
700 2218
6.3%
800 2043
5.8%
900 1926
5.5%
1000 1715
4.9%
ValueCountFrequency (%)
10000 7
< 0.1%
9900 1
 
< 0.1%
9800 5
< 0.1%
9700 3
< 0.1%
9600 1
 
< 0.1%
9500 3
< 0.1%
9400 1
 
< 0.1%
9300 3
< 0.1%
9200 7
< 0.1%
9100 7
< 0.1%

O3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct767
Distinct (%)2.3%
Missing1173
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean55.795044
Minimum0.2142
Maximum415
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:11:09.712224image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.2142
5-th percentile2
Q17
median41
Q381
95-th percentile181
Maximum415
Range414.7858
Interquartile range (IQR)74

Descriptive statistics

Standard deviation57.436983
Coefficient of variation (CV)1.029428
Kurtosis1.8349403
Mean55.795044
Median Absolute Deviation (MAD)36
Skewness1.4020107
Sum1890949.8
Variance3299.007
MonotonicityNot monotonic
2024-03-08T12:11:09.951800image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 4819
 
13.7%
1 1144
 
3.3%
3 692
 
2.0%
4 564
 
1.6%
5 427
 
1.2%
6 410
 
1.2%
7 300
 
0.9%
12 299
 
0.9%
8 292
 
0.8%
9 279
 
0.8%
Other values (757) 24665
70.3%
(Missing) 1173
 
3.3%
ValueCountFrequency (%)
0.2142 21
 
0.1%
0.4284 24
 
0.1%
0.6426 12
 
< 0.1%
0.8568 11
 
< 0.1%
1 1144
3.3%
1.071 12
 
< 0.1%
1.2852 5
 
< 0.1%
1.4994 10
 
< 0.1%
1.7136 3
 
< 0.1%
1.9278 9
 
< 0.1%
ValueCountFrequency (%)
415 1
< 0.1%
356 1
< 0.1%
352 2
< 0.1%
346 1
< 0.1%
340 1
< 0.1%
339 1
< 0.1%
335 2
< 0.1%
332 1
< 0.1%
331 1
< 0.1%
325 1
< 0.1%

TEMP
Real number (ℝ)

HIGH CORRELATION 

Distinct967
Distinct (%)2.8%
Missing20
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean13.584607
Minimum-16.8
Maximum40.5
Zeros196
Zeros (%)0.6%
Negative5308
Negative (%)15.1%
Memory size274.1 KiB
2024-03-08T12:11:10.162061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-16.8
5-th percentile-4
Q13.1
median14.5
Q323.3
95-th percentile30.6
Maximum40.5
Range57.3
Interquartile range (IQR)20.2

Descriptive statistics

Standard deviation11.399097
Coefficient of variation (CV)0.83911861
Kurtosis-1.1573086
Mean13.584607
Median Absolute Deviation (MAD)9.8
Skewness-0.094157845
Sum476058.98
Variance129.93941
MonotonicityNot monotonic
2024-03-08T12:11:10.424479image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 249
 
0.7%
1 240
 
0.7%
2 203
 
0.6%
0 196
 
0.6%
-2 186
 
0.5%
-1 186
 
0.5%
24.1 154
 
0.4%
21.7 150
 
0.4%
-4 143
 
0.4%
22.5 139
 
0.4%
Other values (957) 33198
94.7%
ValueCountFrequency (%)
-16.8 2
< 0.1%
-16.3 1
 
< 0.1%
-16.2 1
 
< 0.1%
-16.1 1
 
< 0.1%
-16 1
 
< 0.1%
-15.9 3
< 0.1%
-15.8 2
< 0.1%
-15.6 1
 
< 0.1%
-15.4 1
 
< 0.1%
-15.3 2
< 0.1%
ValueCountFrequency (%)
40.5 1
< 0.1%
40.3 1
< 0.1%
40.1 1
< 0.1%
39.2 1
< 0.1%
38.8 1
< 0.1%
38.4 1
< 0.1%
38.3 1
< 0.1%
38.1 2
< 0.1%
38 1
< 0.1%
37.9 1
< 0.1%

PRES
Real number (ℝ)

HIGH CORRELATION 

Distinct600
Distinct (%)1.7%
Missing20
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1011.8469
Minimum985.9
Maximum1042
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:11:10.663023image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum985.9
5-th percentile996.1
Q11003.3
median1011.4
Q31020.1
95-th percentile1028.8
Maximum1042
Range56.1
Interquartile range (IQR)16.8

Descriptive statistics

Standard deviation10.404047
Coefficient of variation (CV)0.010282234
Kurtosis-0.88797034
Mean1011.8469
Median Absolute Deviation (MAD)8.5
Skewness0.11150001
Sum35459163
Variance108.24419
MonotonicityNot monotonic
2024-03-08T12:11:10.900828image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1023 256
 
0.7%
1019 252
 
0.7%
1024 249
 
0.7%
1025 248
 
0.7%
1020 239
 
0.7%
1017 238
 
0.7%
1021 237
 
0.7%
1022 237
 
0.7%
1018 233
 
0.7%
1026 224
 
0.6%
Other values (590) 32631
93.1%
ValueCountFrequency (%)
985.9 1
 
< 0.1%
986.3 1
 
< 0.1%
987.2 1
 
< 0.1%
987.5 1
 
< 0.1%
987.7 2
< 0.1%
987.8 3
< 0.1%
987.9 1
 
< 0.1%
988 4
< 0.1%
988.1 4
< 0.1%
988.2 2
< 0.1%
ValueCountFrequency (%)
1042 1
 
< 0.1%
1041.8 1
 
< 0.1%
1041.6 1
 
< 0.1%
1041.4 1
 
< 0.1%
1041.2 2
< 0.1%
1041.1 2
< 0.1%
1041 2
< 0.1%
1040.9 1
 
< 0.1%
1040.8 3
< 0.1%
1040.7 1
 
< 0.1%

DEWP
Real number (ℝ)

HIGH CORRELATION 

Distinct604
Distinct (%)1.7%
Missing20
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean3.1230624
Minimum-35.3
Maximum28.5
Zeros66
Zeros (%)0.2%
Negative14885
Negative (%)42.5%
Memory size274.1 KiB
2024-03-08T12:11:11.166204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-35.3
5-th percentile-19.4
Q1-8.1
median3.8
Q315.6
95-th percentile22.4
Maximum28.5
Range63.8
Interquartile range (IQR)23.7

Descriptive statistics

Standard deviation13.688896
Coefficient of variation (CV)4.3831644
Kurtosis-1.1013944
Mean3.1230624
Median Absolute Deviation (MAD)11.9
Skewness-0.21320853
Sum109444.6
Variance187.38587
MonotonicityNot monotonic
2024-03-08T12:11:11.868296image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.6 143
 
0.4%
17 133
 
0.4%
17.2 129
 
0.4%
18.8 128
 
0.4%
17.8 126
 
0.4%
17.9 125
 
0.4%
18.3 124
 
0.4%
16.8 124
 
0.4%
17.1 124
 
0.4%
18.2 121
 
0.3%
Other values (594) 33767
96.3%
ValueCountFrequency (%)
-35.3 1
< 0.1%
-35.1 1
< 0.1%
-35 1
< 0.1%
-34.8 1
< 0.1%
-34.5 1
< 0.1%
-34.3 2
< 0.1%
-34.2 1
< 0.1%
-34.1 1
< 0.1%
-33.8 1
< 0.1%
-33.7 1
< 0.1%
ValueCountFrequency (%)
28.5 1
 
< 0.1%
27.8 5
< 0.1%
27.7 1
 
< 0.1%
27.6 1
 
< 0.1%
27.5 3
< 0.1%
27.4 1
 
< 0.1%
27.3 4
< 0.1%
27.2 4
< 0.1%
27.1 2
 
< 0.1%
27 6
< 0.1%

RAIN
Real number (ℝ)

SKEWED  ZEROS 

Distinct127
Distinct (%)0.4%
Missing20
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.067420957
Minimum0
Maximum72.5
Zeros33664
Zeros (%)96.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:11:12.107237image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum72.5
Range72.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.91005591
Coefficient of variation (CV)13.498116
Kurtosis1822.1806
Mean0.067420957
Median Absolute Deviation (MAD)0
Skewness34.719336
Sum2362.7
Variance0.82820177
MonotonicityNot monotonic
2024-03-08T12:11:12.408954image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33664
96.0%
0.1 314
 
0.9%
0.2 161
 
0.5%
0.3 107
 
0.3%
0.5 73
 
0.2%
0.4 72
 
0.2%
0.6 67
 
0.2%
0.7 49
 
0.1%
0.9 48
 
0.1%
0.8 39
 
0.1%
Other values (117) 450
 
1.3%
ValueCountFrequency (%)
0 33664
96.0%
0.1 314
 
0.9%
0.2 161
 
0.5%
0.3 107
 
0.3%
0.4 72
 
0.2%
0.5 73
 
0.2%
0.6 67
 
0.2%
0.7 49
 
0.1%
0.8 39
 
0.1%
0.9 48
 
0.1%
ValueCountFrequency (%)
72.5 1
< 0.1%
46.4 1
< 0.1%
40.7 1
< 0.1%
36.6 1
< 0.1%
33.7 1
< 0.1%
33.1 1
< 0.1%
29.3 1
< 0.1%
26.8 1
< 0.1%
24.1 1
< 0.1%
23.7 1
< 0.1%

wd
Categorical

Distinct16
Distinct (%)< 0.1%
Missing81
Missing (%)0.2%
Memory size274.1 KiB
NE
5140 
ENE
3950 
SW
3359 
E
2608 
NNE
2445 
Other values (11)
17481 

Length

Max length3
Median length2
Mean length2.256839
Min length1

Characters and Unicode

Total characters78951
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNNW
2nd rowN
3rd rowNNW
4th rowNW
5th rowN

Common Values

ValueCountFrequency (%)
NE 5140
14.7%
ENE 3950
11.3%
SW 3359
9.6%
E 2608
 
7.4%
NNE 2445
 
7.0%
WSW 2212
 
6.3%
SSW 2098
 
6.0%
N 2066
 
5.9%
NW 1860
 
5.3%
ESE 1717
 
4.9%
Other values (6) 7528
21.5%

Length

2024-03-08T12:11:12.701132image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ne 5140
14.7%
ene 3950
11.3%
sw 3359
9.6%
e 2608
 
7.5%
nne 2445
 
7.0%
wsw 2212
 
6.3%
ssw 2098
 
6.0%
n 2066
 
5.9%
nw 1860
 
5.3%
ese 1717
 
4.9%
Other values (6) 7528
21.5%

Most occurring characters

ValueCountFrequency (%)
E 23890
30.3%
N 22185
28.1%
W 16703
21.2%
S 16173
20.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 78951
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 23890
30.3%
N 22185
28.1%
W 16703
21.2%
S 16173
20.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 78951
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 23890
30.3%
N 22185
28.1%
W 16703
21.2%
S 16173
20.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78951
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 23890
30.3%
N 22185
28.1%
W 16703
21.2%
S 16173
20.5%

WSPM
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct91
Distinct (%)0.3%
Missing14
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.7084964
Minimum0
Maximum11.2
Zeros1353
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:11:12.960350image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.9
median1.4
Q32.2
95-th percentile4.1
Maximum11.2
Range11.2
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.2040711
Coefficient of variation (CV)0.70475481
Kurtosis2.4952557
Mean1.7084964
Median Absolute Deviation (MAD)0.6
Skewness1.365037
Sum59882.8
Variance1.4497871
MonotonicityNot monotonic
2024-03-08T12:11:13.289443image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2 1891
 
5.4%
1.1 1842
 
5.3%
1 1747
 
5.0%
1.3 1640
 
4.7%
0.9 1604
 
4.6%
1.4 1581
 
4.5%
0.8 1443
 
4.1%
1.5 1442
 
4.1%
0.7 1366
 
3.9%
0 1353
 
3.9%
Other values (81) 19141
54.6%
ValueCountFrequency (%)
0 1353
3.9%
0.1 397
 
1.1%
0.2 430
 
1.2%
0.3 191
 
0.5%
0.4 602
 
1.7%
0.5 883
2.5%
0.6 1123
3.2%
0.7 1366
3.9%
0.8 1443
4.1%
0.9 1604
4.6%
ValueCountFrequency (%)
11.2 1
 
< 0.1%
9.2 1
 
< 0.1%
9.1 1
 
< 0.1%
8.9 1
 
< 0.1%
8.8 1
 
< 0.1%
8.6 1
 
< 0.1%
8.5 1
 
< 0.1%
8.4 3
< 0.1%
8.3 2
< 0.1%
8.2 2
< 0.1%

station
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size274.1 KiB
Guanyuan
35064 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters280512
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGuanyuan
2nd rowGuanyuan
3rd rowGuanyuan
4th rowGuanyuan
5th rowGuanyuan

Common Values

ValueCountFrequency (%)
Guanyuan 35064
100.0%

Length

2024-03-08T12:11:13.631674image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T12:11:13.813093image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
guanyuan 35064
100.0%

Most occurring characters

ValueCountFrequency (%)
u 70128
25.0%
a 70128
25.0%
n 70128
25.0%
G 35064
12.5%
y 35064
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 245448
87.5%
Uppercase Letter 35064
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 70128
28.6%
a 70128
28.6%
n 70128
28.6%
y 35064
14.3%
Uppercase Letter
ValueCountFrequency (%)
G 35064
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 280512
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 70128
25.0%
a 70128
25.0%
n 70128
25.0%
G 35064
12.5%
y 35064
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 280512
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 70128
25.0%
a 70128
25.0%
n 70128
25.0%
G 35064
12.5%
y 35064
12.5%

Interactions

2024-03-08T12:11:00.062229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:25.685399image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:27.980409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:30.371468image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:32.643021image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:34.905915image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:37.243012image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:39.782931image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:41.925370image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:44.051248image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:46.275017image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:48.623153image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:50.721036image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:53.575632image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:56.995865image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:00.227412image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:25.842518image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:28.145458image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:30.524994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:32.803101image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:35.061644image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:37.393521image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:39.930035image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:42.065736image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:44.181313image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:46.609963image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:48.749822image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:50.863324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:53.793993image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:57.221261image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:00.367268image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:25.960487image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:28.266995image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:30.666942image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:32.949257image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:35.221445image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:37.532133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:40.080656image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:42.195207image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:44.319171image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:46.743532image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:48.877974image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:51.003119image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:53.946769image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:57.464091image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:00.513755image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:26.126271image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:28.409264image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:30.809410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:33.094550image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:35.369709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:37.670703image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:40.223733image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:42.332065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:44.447486image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:46.883634image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:49.000242image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:51.154309image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:54.105093image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:57.663262image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:00.643723image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:26.268528image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:28.553636image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:30.946066image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:33.225155image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:35.497746image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:37.838389image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:40.347280image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:42.459447image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:44.583035image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:47.009044image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:49.134682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:51.285859image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:54.310151image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:57.819691image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:00.833878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:26.424058image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:28.725128image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:31.091600image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:33.373357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:35.670671image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:38.000097image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:40.490840image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:42.605291image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:44.724675image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:47.154454image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:49.294093image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:51.434384image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:54.560188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:58.076084image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:00.968337image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:26.581172image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:28.869603image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:31.226967image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:33.520134image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:35.818968image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:38.139891image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:40.616242image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:42.749370image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:44.863225image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:47.286517image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:49.436939image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:51.642067image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:54.802336image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:58.300047image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:01.155237image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:26.768310image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:29.032527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:31.427933image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:33.737836image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:35.970852image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:38.598227image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:40.767765image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:42.901385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:45.017770image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:47.415298image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:49.568450image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:51.848604image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:54.998650image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:58.472193image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:01.309931image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:26.931924image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:29.181488image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:31.599756image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:33.906879image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:36.136758image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:38.771861image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:40.940750image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:43.034200image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:45.158977image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:47.551860image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:49.706825image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:52.090162image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:55.175578image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:58.676710image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:01.432639image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:27.101209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:29.330680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:31.753438image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:34.065190image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:36.320526image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:38.912068image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:41.075386image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:43.201326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:45.299183image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:47.679214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:49.845193image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:52.324425image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:55.326687image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:58.877037image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:01.605636image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:27.240324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:29.672823image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:31.881273image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:34.197325image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:36.449247image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:39.066395image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:41.181637image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:43.345671image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:45.437188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:47.832962image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:49.984478image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:52.518028image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:55.518155image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:59.026397image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:01.761409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:27.393247image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:29.817067image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:32.030142image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:34.326679image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:36.587724image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:39.215518image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:41.334454image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:43.488431image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:45.712113image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:48.064173image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:50.125256image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:52.780654image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:55.764451image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:59.220984image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:01.955786image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:27.560608image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:29.953593image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:32.191168image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:34.470691image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:36.739872image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:39.356317image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:41.488608image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:43.613926image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:45.855188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:48.220598image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:50.267622image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:52.958227image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:56.445283image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:59.444980image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:02.139559image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:27.710245image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:30.091421image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:32.345682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:34.613378image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:36.918439image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:39.500338image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:41.627495image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:43.758806image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:45.977335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:48.360230image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:50.421108image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:53.107930image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:56.613097image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:59.667213image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:11:02.383581image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:27.851983image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:30.239201image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:32.504138image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:34.773864image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:37.083023image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:39.649036image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:41.773558image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:43.903505image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:46.124877image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:48.488266image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:50.596946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:53.318475image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:56.794850image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:10:59.861639image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-03-08T12:11:13.986748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
CODEWPNO2NoO3PM10PM2.5PRESRAINSO2TEMPWSPMdayhourmonthwdyear
CO1.0000.1190.753-0.034-0.4400.7380.8460.0670.0080.592-0.195-0.4120.010-0.0400.0670.1080.077
DEWP0.1191.000-0.041-0.1330.2910.1680.258-0.7820.174-0.3180.821-0.2550.023-0.0130.2600.1160.158
NO20.753-0.0411.000-0.057-0.6600.6240.6540.168-0.0840.542-0.325-0.5160.012-0.0670.0580.1260.073
No-0.034-0.133-0.0571.000-0.054-0.060-0.0540.2280.001-0.229-0.1030.1110.0180.0010.0440.1170.862
O3-0.4400.291-0.660-0.0541.000-0.184-0.222-0.450-0.003-0.2390.6060.411-0.0050.278-0.1430.1640.045
PM100.7380.1680.624-0.060-0.1841.0000.895-0.117-0.0830.531-0.006-0.2520.0340.042-0.0210.0840.054
PM2.50.8460.2580.654-0.054-0.2220.8951.000-0.107-0.0180.544-0.009-0.3390.0150.0020.0230.0990.053
PRES0.067-0.7820.1680.228-0.450-0.117-0.1071.000-0.0850.265-0.8310.0490.013-0.0360.0030.0760.168
RAIN0.0080.174-0.0840.001-0.003-0.083-0.018-0.0851.000-0.1650.041-0.021-0.005-0.0040.0430.0040.009
SO20.592-0.3180.542-0.229-0.2390.5310.5440.265-0.1651.000-0.384-0.121-0.0020.003-0.1960.0510.100
TEMP-0.1950.821-0.325-0.1030.606-0.006-0.009-0.8310.041-0.3841.0000.0970.0170.1450.1230.1040.147
WSPM-0.412-0.255-0.5160.1110.411-0.252-0.3390.049-0.021-0.1210.0971.000-0.0080.171-0.1780.1590.077
day0.0100.0230.0120.018-0.0050.0340.0150.013-0.005-0.0020.017-0.0081.0000.0000.0100.0270.000
hour-0.040-0.013-0.0670.0010.2780.0420.002-0.036-0.0040.0030.1450.1710.0001.0000.0000.1310.000
month0.0670.2600.0580.044-0.143-0.0210.0230.0030.043-0.1960.123-0.1780.0100.0001.0000.0870.249
wd0.1080.1160.1260.1170.1640.0840.0990.0760.0040.0510.1040.1590.0270.1310.0871.0000.131
year0.0770.1580.0730.8620.0450.0540.0530.1680.0090.1000.1470.0770.0000.0000.2490.1311.000

Missing values

2024-03-08T12:11:02.603426image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-08T12:11:03.077253image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-08T12:11:03.522161image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

NoyearmonthdayhourPM2.5PM10SO2NO2COO3TEMPPRESDEWPRAINwdWSPMstation
0120133104.04.014.020.0300.069.0-0.71023.0-18.80.0NNW4.4Guanyuan
1220133114.04.013.017.0300.072.0-1.11023.2-18.20.0N4.7Guanyuan
2320133123.03.010.019.0300.069.0-1.11023.5-18.20.0NNW5.6Guanyuan
3420133133.06.07.024.0400.062.0-1.41024.5-19.40.0NW3.1Guanyuan
4520133143.06.05.014.0400.071.0-2.01025.2-19.50.0N2.0Guanyuan
5620133153.06.06.014.0400.071.0-2.21025.6-19.60.0N3.7Guanyuan
6720133166.06.06.020.0400.066.0-2.61026.5-19.10.0NNE2.5Guanyuan
7820133173.03.07.026.0400.061.0-1.61027.4-19.10.0NNW3.8Guanyuan
8920133183.06.09.037.0500.050.00.11028.3-19.20.0NNW4.1Guanyuan
91020133197.011.09.030.0400.058.01.21028.5-19.30.0N2.6Guanyuan
NoyearmonthdayhourPM2.5PM10SO2NO2COO3TEMPPRESDEWPRAINwdWSPMstation
35054350552017228143.074.02.015.0200.084.014.61013.3-15.60.0N3.6Guanyuan
35055350562017228158.025.02.017.0200.087.015.41013.0-15.00.0NNW3.3Guanyuan
35056350572017228168.017.02.016.0300.088.014.91012.6-15.40.0NW2.1Guanyuan
350573505820172281711.023.03.019.0300.083.014.21012.5-14.90.0NW3.1Guanyuan
350583505920172281810.033.02.022.0300.078.013.41013.0-15.50.0WNW1.4Guanyuan
350593506020172281913.037.03.036.0400.060.012.51013.5-16.20.0NW2.4Guanyuan
350603506120172282020.043.04.048.0500.043.011.61013.6-15.10.0WNW0.9Guanyuan
350613506220172282116.033.05.039.0500.050.010.81014.2-13.30.0NW1.1Guanyuan
350623506320172282211.024.05.047.0500.041.010.51014.4-12.90.0NNW1.2Guanyuan
350633506420172282315.027.05.053.0600.033.08.61014.1-15.90.0NNE1.3Guanyuan